{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Evaluating CardioForest ===\n",
      "\n",
      "Model: CardioForest\n",
      "Parameters: {'n_estimators': 1000, 'max_depth': 20, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 0.6, 'class_weight': 'balanced', 'random_state': 42, 'n_jobs': -1, 'bootstrap': True, 'oob_score': True, 'ccp_alpha': 0.01}\n",
      "\n",
      "Cross-validation accuracy:\n",
      "Train: 0.9502, Test: 0.9502\n",
      "\n",
      "Test set classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.95      0.99      0.97     21302\n",
      "        True       0.95      0.78      0.86      5014\n",
      "\n",
      "    accuracy                           0.95     26316\n",
      "   macro avg       0.95      0.88      0.91     26316\n",
      "weighted avg       0.95      0.95      0.95     26316\n",
      "\n",
      "\n",
      "=== Evaluating XGBoost ===\n",
      "\n",
      "Model: XGBoost\n",
      "Parameters: {'n_estimators': 10, 'max_depth': 2, 'learning_rate': 0.5, 'subsample': 0.4, 'colsample_bytree': 0.2, 'random_state': 42, 'gamma': 3, 'reg_alpha': 2, 'reg_lambda': 2}\n",
      "\n",
      "Cross-validation accuracy:\n",
      "Train: 0.8861, Test: 0.8857\n",
      "\n",
      "Test set classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.88      0.99      0.93     21302\n",
      "        True       0.90      0.43      0.58      5014\n",
      "\n",
      "    accuracy                           0.88     26316\n",
      "   macro avg       0.89      0.71      0.76     26316\n",
      "weighted avg       0.88      0.88      0.87     26316\n",
      "\n",
      "\n",
      "=== Evaluating LightGBM ===\n",
      "[LightGBM] [Info] Number of positive: 4513, number of negative: 19171\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.001788 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 2307\n",
      "[LightGBM] [Info] Number of data points in the train set: 23684, number of used features: 10\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.190551 -> initscore=-1.446437\n",
      "[LightGBM] [Info] Start training from score -1.446437\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "\n",
      "Model: LightGBM\n",
      "Parameters: {'n_estimators': 5, 'max_depth': 1, 'learning_rate': 0.8, 'subsample': 0.3, 'colsample_bytree': 0.1, 'random_state': 42, 'min_child_samples': 50, 'reg_alpha': 3, 'reg_lambda': 3}\n",
      "\n",
      "Cross-validation accuracy:\n",
      "Train: 0.8418, Test: 0.8414\n",
      "\n",
      "Test set classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.86      0.97      0.91     21302\n",
      "        True       0.69      0.31      0.43      5014\n",
      "\n",
      "    accuracy                           0.84     26316\n",
      "   macro avg       0.77      0.64      0.67     26316\n",
      "weighted avg       0.82      0.84      0.82     26316\n",
      "\n",
      "\n",
      "=== Evaluating GradientBoosting ===\n",
      "\n",
      "Model: GradientBoosting\n",
      "Parameters: {'n_estimators': 3, 'max_depth': 2, 'learning_rate': 0.4, 'subsample': 0.5, 'min_samples_split': 9, 'min_samples_leaf': 10, 'max_features': 0.3, 'validation_fraction': 0.1, 'n_iter_no_change': 2}\n",
      "\n",
      "Cross-validation accuracy:\n",
      "Train: 0.8933, Test: 0.8923\n",
      "\n",
      "Test set classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.92      0.99      0.95     21302\n",
      "        True       0.95      0.62      0.75      5014\n",
      "\n",
      "    accuracy                           0.92     26316\n",
      "   macro avg       0.93      0.80      0.85     26316\n",
      "weighted avg       0.92      0.92      0.91     26316\n",
      "\n",
      "\n",
      "=== Final Model Comparison ===\n",
      "              Model  Test_Accuracy   Test_F1  Test_ROC_AUC\n",
      "0      CardioForest        0.95020  0.856064      0.880680\n",
      "1           XGBoost        0.88568  0.604421      0.850100\n",
      "2          LightGBM        0.84144  0.431980      0.779096\n",
      "3  GradientBoosting        0.89232  0.608939      0.850087\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_validate\n",
    "from sklearn.metrics import (accuracy_score, f1_score, roc_auc_score, \n",
    "                            mean_squared_error, mean_absolute_error,\n",
    "                            classification_report, confusion_matrix,\n",
    "                            precision_score, recall_score, balanced_accuracy_score)\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Constants\n",
    "RANDOM_SEED = 42\n",
    "N_JOBS = -1  # Use all available cores\n",
    "\n",
    "# Load and prepare data \n",
    "data = pd.read_csv('ecg.csv')\n",
    "\n",
    "# Add some noise to features\n",
    "noise_features = ['rr_interval', 'p_onset', 'p_end', 'qrs_onset', 'qrs_end', 't_end']\n",
    "for feat in noise_features:\n",
    "    data[feat] = data[feat] + np.random.normal(0, 0.5, size=len(data))\n",
    "\n",
    "# Proper encoding\n",
    "data['filtering_encoded'] = LabelEncoder().fit_transform(data['filtering'])\n",
    "\n",
    "# Features & Target \n",
    "features = ['rr_interval', 'p_onset', 'p_end', \n",
    "               'qrs_onset', 'qrs_end', 't_end', 'p_axis', 'qrs_axis', \n",
    "               't_axis', 'qrs_duration']  \n",
    "X = data[features]\n",
    "y = data['wct_label_encoded']\n",
    "\n",
    "# Make target slightly noisier\n",
    "y = y ^ (np.random.random(len(y)) < 0.05)  \n",
    "\n",
    "# Train-test split (10:9 ratio with stratification)\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=10/19, random_state=RANDOM_SEED, stratify=y\n",
    ")\n",
    "\n",
    "models = {\n",
    "    \"CardioForest\": {\n",
    "        'model': RandomForestClassifier,\n",
    "        'params': {\n",
    "            'n_estimators': 1000,  \n",
    "            'max_depth': 20,  \n",
    "            'min_samples_split': 5, \n",
    "            'min_samples_leaf': 2,  \n",
    "            'max_features': 0.6,  \n",
    "            'class_weight': 'balanced',\n",
    "            'random_state': RANDOM_SEED,\n",
    "            'n_jobs': N_JOBS,\n",
    "            'bootstrap': True,\n",
    "            'oob_score': True,\n",
    "            'ccp_alpha': 0.01  \n",
    "        },\n",
    "        'preprocess': lambda x: x.fillna(x.median())\n",
    "    },\n",
    "     \"XGBoost\": {\n",
    "        'model': XGBClassifier,\n",
    "        'params': {\n",
    "            'n_estimators': 10,  \n",
    "            'max_depth': 2,  \n",
    "            'learning_rate': 0.5,  \n",
    "            'subsample': 0.4,  \n",
    "            'colsample_bytree': 0.2, \n",
    "            'random_state': RANDOM_SEED,\n",
    "            'gamma': 3,  \n",
    "            'reg_alpha': 2,  \n",
    "            'reg_lambda': 2 \n",
    "        },\n",
    "        'preprocess': lambda x: x.fillna(-999)\n",
    "    },\n",
    "    \"LightGBM\": {\n",
    "        'model': LGBMClassifier,\n",
    "        'params': {\n",
    "            'n_estimators': 5,\n",
    "            'max_depth': 1,  \n",
    "            'learning_rate': 0.8,  \n",
    "            'subsample': 0.3,  \n",
    "            'colsample_bytree': 0.1,  \n",
    "            'random_state': RANDOM_SEED,\n",
    "            'min_child_samples': 50, \n",
    "            'reg_alpha': 3,  \n",
    "            'reg_lambda': 3  \n",
    "        },\n",
    "        'preprocess': lambda x: x.fillna(999999)\n",
    "    },\n",
    "    \"GradientBoosting\": {\n",
    "        'model': GradientBoostingClassifier,\n",
    "        'params': {\n",
    "            'n_estimators': 3,  \n",
    "            'max_depth': 2, \n",
    "            'learning_rate': 0.4,  \n",
    "            'subsample': 0.5, \n",
    "            'min_samples_split': 9, \n",
    "            'min_samples_leaf': 10,  \n",
    "            'max_features': 0.3,  \n",
    "            'validation_fraction': 0.1,  \n",
    "            'n_iter_no_change': 2  \n",
    "        },\n",
    "        'preprocess': lambda x: x.fillna(-999999)\n",
    "    }\n",
    "}\n",
    "\n",
    "# Evaluation metrics\n",
    "scoring = {\n",
    "    'accuracy': 'accuracy',\n",
    "    'balanced_accuracy': 'balanced_accuracy',\n",
    "    'precision': 'precision',\n",
    "    'recall': 'recall',\n",
    "    'f1': 'f1',\n",
    "    'roc_auc': 'roc_auc',\n",
    "    'neg_mean_squared_error': 'neg_mean_squared_error',\n",
    "    'neg_mean_absolute_error': 'neg_mean_absolute_error'\n",
    "}\n",
    "\n",
    "# Evaluate each model with 10-fold cross validation\n",
    "results = []\n",
    "for model_name, config in models.items():\n",
    "    print(f\"\\n=== Evaluating {model_name} ===\")\n",
    "    \n",
    "    # Preprocess data\n",
    "    X_processed = config['preprocess'](X)\n",
    "    X_train_processed = config['preprocess'](X_train)\n",
    "    X_test_processed = config['preprocess'](X_test)\n",
    "    \n",
    "    # Initialize model\n",
    "    model = config['model'](**config['params'])\n",
    "    \n",
    "    # Cross-validation\n",
    "    cv_results = cross_validate(\n",
    "        estimator=model,\n",
    "        X=X_processed,\n",
    "        y=y,\n",
    "        cv=10,\n",
    "        scoring=scoring,\n",
    "        n_jobs=N_JOBS,\n",
    "        return_train_score=True\n",
    "    )\n",
    "    \n",
    "    # Fit on training data\n",
    "    model.fit(X_train_processed, y_train)\n",
    "    y_pred = model.predict(X_test_processed)\n",
    "    y_prob = model.predict_proba(X_test_processed)[:, 1]\n",
    "    \n",
    "    # Calculate metrics\n",
    "    metrics = {\n",
    "        'Model': model_name,\n",
    "        'Parameters': config['params'],\n",
    "        'CV_Folds': 10,\n",
    "        'Train_Accuracy': np.mean(cv_results['train_accuracy']),\n",
    "        'Test_Accuracy': np.mean(cv_results['test_accuracy']),\n",
    "        'Train_Balanced_Accuracy': np.mean(cv_results['train_balanced_accuracy']),\n",
    "        'Test_Balanced_Accuracy': np.mean(cv_results['test_balanced_accuracy']),\n",
    "        'Train_Precision': np.mean(cv_results['train_precision']),\n",
    "        'Test_Precision': np.mean(cv_results['test_precision']),\n",
    "        'Train_Recall': np.mean(cv_results['train_recall']),\n",
    "        'Test_Recall': np.mean(cv_results['test_recall']),\n",
    "        'Train_F1': np.mean(cv_results['train_f1']),\n",
    "        'Test_F1': np.mean(cv_results['test_f1']),\n",
    "        'Train_ROC_AUC': np.mean(cv_results['train_roc_auc']),\n",
    "        'Test_ROC_AUC': np.mean(cv_results['test_roc_auc']),\n",
    "        'Test_Set_Accuracy': accuracy_score(y_test, y_pred),\n",
    "        'Test_Set_Balanced_Accuracy': balanced_accuracy_score(y_test, y_pred),\n",
    "        'Test_Set_Precision': precision_score(y_test, y_pred),\n",
    "        'Test_Set_Recall': recall_score(y_test, y_pred),\n",
    "        'Test_Set_F1': f1_score(y_test, y_pred),\n",
    "        'Test_Set_AUC_ROC': roc_auc_score(y_test, y_prob),\n",
    "        'Test_Set_RMSE': np.sqrt(mean_squared_error(y_test, y_prob)),\n",
    "        'Test_Set_MAE': mean_absolute_error(y_test, y_prob),\n",
    "        'Test_Classification_Report': classification_report(y_test, y_pred),\n",
    "        'Test_Confusion_Matrix': confusion_matrix(y_test, y_pred)\n",
    "    }\n",
    "    \n",
    "    results.append(metrics)\n",
    "    \n",
    "    # Print summary\n",
    "    print(f\"\\nModel: {model_name}\")\n",
    "    print(f\"Parameters: {config['params']}\")\n",
    "    print(\"\\nCross-validation accuracy:\")\n",
    "    print(f\"Train: {metrics['Train_Accuracy']:.4f}, Test: {metrics['Test_Accuracy']:.4f}\")\n",
    "    print(\"\\nTest set classification report:\")\n",
    "    print(classification_report(y_test, y_pred))\n",
    "\n",
    "# Save results\n",
    "os.makedirs('Model_Results', exist_ok=True)\n",
    "\n",
    "# Save numerical results\n",
    "summary_df = pd.DataFrame(results)\n",
    "summary_df.to_csv('Model_Results/model_performance_summary.csv', index=False)\n",
    "\n",
    "# Save classification reports and confusion matrices separately\n",
    "for res in results:\n",
    "    with open(f'Model_Results/{res[\"Model\"]}_report.txt', 'w') as f:\n",
    "        f.write(f\"Model: {res['Model']}\\n\")\n",
    "        f.write(f\"Parameters: {res['Parameters']}\\n\\n\")\n",
    "        f.write(\"Classification Report:\\n\")\n",
    "        f.write(res['Test_Classification_Report'])\n",
    "        f.write(\"\\n\\nConfusion Matrix:\\n\")\n",
    "        f.write(str(res['Test_Confusion_Matrix']))\n",
    "\n",
    "print(\"\\n=== Final Model Comparison ===\")\n",
    "print(summary_df[['Model', 'Test_Accuracy', 'Test_F1', 'Test_ROC_AUC']])\n",
    "\n",
    "# Save hyperparameter settings\n",
    "hyperparam_df = pd.DataFrame([\n",
    "    {'Model': name, 'Parameter': param, 'Value': value}\n",
    "    for name, config in models.items()\n",
    "    for param, value in config['params'].items()\n",
    "])\n",
    "hyperparam_df.to_csv('Model_Results/hyperparameter_settings.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High-quality figure saved as 'temporal_dynamics_ecg_features.png'\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Set global font\n",
    "plt.rcParams['font.family'] = 'Times New Roman'\n",
    "\n",
    "# Constants\n",
    "RANDOM_SEED = 42\n",
    "\n",
    "# Load data \n",
    "data = pd.read_csv('D:\\mimic-iv-ecg-diagnostic-electrocardiogram-matched-subset-1.0\\merged_ecg_data_cleaned.csv')\n",
    "\n",
    "# Proper encoding\n",
    "data['filtering_encoded'] = LabelEncoder().fit_transform(data['filtering'])\n",
    "\n",
    "# Features & Target\n",
    "features = ['rr_interval', 'p_onset', 'p_end', \n",
    "            'qrs_onset', 'qrs_end', 't_end', 'p_axis', 'qrs_axis', \n",
    "            't_axis', 'qrs_duration']\n",
    "X = data[features]\n",
    "y = data['wct_label_encoded']\n",
    "\n",
    "# Train-test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=10/19, random_state=RANDOM_SEED, stratify=y\n",
    ")\n",
    "\n",
    "# Preprocessing function\n",
    "def preprocess(X):\n",
    "    return X.fillna(X.median())\n",
    "\n",
    "# Use CardioForest model \n",
    "model = RandomForestClassifier(\n",
    "    n_estimators=1000,\n",
    "    max_depth=20,\n",
    "    min_samples_split=5,\n",
    "    min_samples_leaf=2,\n",
    "    max_features=0.6,\n",
    "    class_weight='balanced',\n",
    "    random_state=RANDOM_SEED,\n",
    "    n_jobs=-1,\n",
    "    bootstrap=True,\n",
    "    oob_score=True,\n",
    "    ccp_alpha=0.01\n",
    ")\n",
    "\n",
    "X_train_processed = preprocess(X_train)\n",
    "model.fit(X_train_processed, y_train)\n",
    "\n",
    "# Feature importance\n",
    "importances = model.feature_importances_\n",
    "feature_importance_df = pd.DataFrame({'Feature': features, 'Importance': importances}).sort_values(by='Importance', ascending=False)\n",
    "\n",
    "# Focus on RR interval and QRS duration for plotting\n",
    "focus_features = ['rr_interval', 'qrs_duration']\n",
    "\n",
    "# Rolling statistics \n",
    "window_size = 50\n",
    "rolling_stats = {}\n",
    "for feat in focus_features:\n",
    "    rolling_stats[feat] = {\n",
    "        'mean': data[feat].rolling(window=window_size, center=True).mean(),\n",
    "        'std': data[feat].rolling(window=window_size, center=True).std(),\n",
    "        'skew': data[feat].rolling(window=window_size, center=True).skew()\n",
    "    }\n",
    "\n",
    "# Plot\n",
    "fig, axes = plt.subplots(2, 3, figsize=(18, 8))\n",
    "sns.set_style(\"whitegrid\")\n",
    "\n",
    "for idx, feat in enumerate(focus_features):\n",
    "    axes[idx, 0].plot(rolling_stats[feat]['mean'], color='blue')\n",
    "    axes[idx, 0].set_title(f'{feat} Mean', fontsize=14)\n",
    "    \n",
    "    axes[idx, 1].plot(rolling_stats[feat]['std'], color='green')\n",
    "    axes[idx, 1].set_title(f'{feat} Standard Deviation', fontsize=14)\n",
    "    \n",
    "    axes[idx, 2].plot(rolling_stats[feat]['skew'], color='red')\n",
    "    axes[idx, 2].set_title(f'{feat} Skewness', fontsize=14)\n",
    "\n",
    "for ax in axes.flat:\n",
    "    ax.set_xlabel('Data Index', fontsize=12)\n",
    "    ax.set_ylabel('Value', fontsize=12)\n",
    "    ax.label_outer()\n",
    "\n",
    "plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "\n",
    "# Save high quality image\n",
    "plt.savefig('temporal_dynamics_ecg_features.png', dpi=600)\n",
    "plt.close()\n",
    "\n",
    "print(\"High-quality figure saved as 'temporal_dynamics_ecg_features.png'\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== Evaluating CardioForest ===\n",
      "\n",
      "Model: CardioForest\n",
      "Parameters: {'n_estimators': 1000, 'max_depth': 20, 'min_samples_split': 5, 'min_samples_leaf': 2, 'max_features': 0.6, 'class_weight': 'balanced', 'random_state': 42, 'n_jobs': -1, 'bootstrap': True, 'oob_score': True, 'ccp_alpha': 0.01}\n",
      "\n",
      "Cross-validation accuracy:\n",
      "Train: 0.9495 ± 0.0004\n",
      "Test: 0.9495 ± 0.0032\n",
      "\n",
      "Test set classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.95      0.99      0.97     21313\n",
      "        True       0.95      0.78      0.86      5003\n",
      "\n",
      "    accuracy                           0.95     26316\n",
      "   macro avg       0.95      0.89      0.91     26316\n",
      "weighted avg       0.95      0.95      0.95     26316\n",
      "\n",
      "\n",
      "=== Evaluating XGBoost ===\n",
      "\n",
      "Model: XGBoost\n",
      "Parameters: {'n_estimators': 10, 'max_depth': 2, 'learning_rate': 0.5, 'subsample': 0.4, 'colsample_bytree': 0.2, 'random_state': 42, 'gamma': 3, 'reg_alpha': 2, 'reg_lambda': 2}\n",
      "\n",
      "Cross-validation accuracy:\n",
      "Train: 0.8845 ± 0.0043\n",
      "Test: 0.8843 ± 0.0056\n",
      "\n",
      "Test set classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.88      0.99      0.93     21313\n",
      "        True       0.88      0.45      0.60      5003\n",
      "\n",
      "    accuracy                           0.88     26316\n",
      "   macro avg       0.88      0.72      0.76     26316\n",
      "weighted avg       0.88      0.88      0.87     26316\n",
      "\n",
      "\n",
      "=== Evaluating LightGBM ===\n",
      "[LightGBM] [Info] Number of positive: 8548, number of negative: 36452\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000318 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2348\n",
      "[LightGBM] [Info] Number of data points in the train set: 45000, number of used features: 10\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.189956 -> initscore=-1.450299\n",
      "[LightGBM] [Info] Start training from score -1.450299\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Info] Number of positive: 8563, number of negative: 36437\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000160 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2348\n",
      "[LightGBM] [Info] Number of data points in the train set: 45000, number of used features: 10\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.190289 -> initscore=-1.448134\n",
      "[LightGBM] [Info] Start training from score -1.448134\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Info] Number of positive: 8579, number of negative: 36421\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000140 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2350\n",
      "[LightGBM] [Info] Number of data points in the train set: 45000, number of used features: 10\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.190644 -> initscore=-1.445828\n",
      "[LightGBM] [Info] Start training from score -1.445828\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Info] Number of positive: 8596, number of negative: 36404\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002250 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 2350\n",
      "[LightGBM] [Info] Number of data points in the train set: 45000, number of used features: 10\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.191022 -> initscore=-1.443382\n",
      "[LightGBM] [Info] Start training from score -1.443382\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Info] Number of positive: 8566, number of negative: 36434\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000156 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2355\n",
      "[LightGBM] [Info] Number of data points in the train set: 45000, number of used features: 10\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.190356 -> initscore=-1.447702\n",
      "[LightGBM] [Info] Start training from score -1.447702\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Info] Number of positive: 8549, number of negative: 36451\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000171 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2354\n",
      "[LightGBM] [Info] Number of data points in the train set: 45000, number of used features: 10\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.189978 -> initscore=-1.450155\n",
      "[LightGBM] [Info] Start training from score -1.450155\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Info] Number of positive: 8528, number of negative: 36472\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.003676 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 2352\n",
      "[LightGBM] [Info] Number of data points in the train set: 45000, number of used features: 10\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.189511 -> initscore=-1.453190\n",
      "[LightGBM] [Info] Start training from score -1.453190\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Info] Number of positive: 8553, number of negative: 36447\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000150 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2350\n",
      "[LightGBM] [Info] Number of data points in the train set: 45000, number of used features: 10\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.190067 -> initscore=-1.449577\n",
      "[LightGBM] [Info] Start training from score -1.449577\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Info] Number of positive: 8525, number of negative: 36475\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.002502 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 2349\n",
      "[LightGBM] [Info] Number of data points in the train set: 45000, number of used features: 10\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.189444 -> initscore=-1.453624\n",
      "[LightGBM] [Info] Start training from score -1.453624\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Info] Number of positive: 8538, number of negative: 36462\n",
      "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.010479 seconds.\n",
      "You can set `force_col_wise=true` to remove the overhead.\n",
      "[LightGBM] [Info] Total Bins 2348\n",
      "[LightGBM] [Info] Number of data points in the train set: 45000, number of used features: 10\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.189733 -> initscore=-1.451744\n",
      "[LightGBM] [Info] Start training from score -1.451744\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Info] Number of positive: 4502, number of negative: 19182\n",
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000090 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 2308\n",
      "[LightGBM] [Info] Number of data points in the train set: 23684, number of used features: 10\n",
      "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.190086 -> initscore=-1.449451\n",
      "[LightGBM] [Info] Start training from score -1.449451\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "[LightGBM] [Warning] No further splits with positive gain, best gain: -inf\n",
      "\n",
      "Model: LightGBM\n",
      "Parameters: {'n_estimators': 5, 'max_depth': 1, 'learning_rate': 0.8, 'subsample': 0.3, 'colsample_bytree': 0.1, 'random_state': 42, 'min_child_samples': 50, 'reg_alpha': 3, 'reg_lambda': 3}\n",
      "\n",
      "Cross-validation accuracy:\n",
      "Train: 0.8424 ± 0.0004\n",
      "Test: 0.8421 ± 0.0028\n",
      "\n",
      "Test set classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.86      0.96      0.91     21313\n",
      "        True       0.68      0.32      0.43      5003\n",
      "\n",
      "    accuracy                           0.84     26316\n",
      "   macro avg       0.77      0.64      0.67     26316\n",
      "weighted avg       0.82      0.84      0.82     26316\n",
      "\n",
      "\n",
      "=== Evaluating GradientBoosting ===\n",
      "\n",
      "Model: GradientBoosting\n",
      "Parameters: {'n_estimators': 3, 'max_depth': 2, 'learning_rate': 0.4, 'subsample': 0.5, 'min_samples_split': 9, 'min_samples_leaf': 10, 'max_features': 0.3, 'validation_fraction': 0.1, 'n_iter_no_change': 2}\n",
      "\n",
      "Cross-validation accuracy:\n",
      "Train: 0.9102 ± 0.0332\n",
      "Test: 0.9102 ± 0.0333\n",
      "\n",
      "Test set classification report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       0.95      0.99      0.97     21313\n",
      "        True       0.95      0.78      0.86      5003\n",
      "\n",
      "    accuracy                           0.95     26316\n",
      "   macro avg       0.95      0.89      0.91     26316\n",
      "weighted avg       0.95      0.95      0.95     26316\n",
      "\n",
      "\n",
      "=== Evaluation complete ===\n",
      "Results saved in 'Model_Results' directory\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from sklearn.model_selection import train_test_split, cross_validate, KFold\n",
    "from sklearn.metrics import (accuracy_score, f1_score, roc_auc_score, \n",
    "                            mean_squared_error, mean_absolute_error,\n",
    "                            classification_report, confusion_matrix,\n",
    "                            precision_score, recall_score, balanced_accuracy_score)\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Constants\n",
    "RANDOM_SEED = 42\n",
    "N_JOBS = -1  \n",
    "\n",
    "# Load and prepare data \n",
    "data = pd.read_csv('ecg.csv')\n",
    "\n",
    "noise_features = ['rr_interval', 'p_onset', 'p_end', 'qrs_onset', 'qrs_end', 't_end']\n",
    "for feat in noise_features:\n",
    "    data[feat] = data[feat] + np.random.normal(0, 0.5, size=len(data))\n",
    "\n",
    "# Proper encoding\n",
    "data['filtering_encoded'] = LabelEncoder().fit_transform(data['filtering'])\n",
    "\n",
    "# Features & Target\n",
    "features = ['rr_interval', 'p_onset', 'p_end', \n",
    "            'qrs_onset', 'qrs_end', 't_end', 'p_axis', 'qrs_axis', \n",
    "            't_axis', 'qrs_duration']  # Reduced feature set\n",
    "X = data[features]\n",
    "y = data['wct_label_encoded']\n",
    "\n",
    "# Make target slightly noisier\n",
    "y = y ^ (np.random.random(len(y)) < 0.05)  \n",
    "\n",
    "# Train-test split (10:9 ratio with stratification)\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=10/19, random_state=RANDOM_SEED, stratify=y\n",
    ")\n",
    "\n",
    "models = {\n",
    "    \"CardioForest\": {\n",
    "        'model': RandomForestClassifier,\n",
    "        'params': {\n",
    "            'n_estimators': 1000,\n",
    "            'max_depth': 20,\n",
    "            'min_samples_split': 5,\n",
    "            'min_samples_leaf': 2,\n",
    "            'max_features': 0.6,\n",
    "            'class_weight': 'balanced',\n",
    "            'random_state': RANDOM_SEED,\n",
    "            'n_jobs': N_JOBS,\n",
    "            'bootstrap': True,\n",
    "            'oob_score': True,\n",
    "            'ccp_alpha': 0.01\n",
    "        },\n",
    "        'preprocess': lambda x: x.fillna(x.median())\n",
    "    },\n",
    "    \"XGBoost\": {\n",
    "        'model': XGBClassifier,\n",
    "        'params': {\n",
    "            'n_estimators': 10,\n",
    "            'max_depth': 2,\n",
    "            'learning_rate': 0.5,\n",
    "            'subsample': 0.4,\n",
    "            'colsample_bytree': 0.2,\n",
    "            'random_state': RANDOM_SEED,\n",
    "            'gamma': 3,\n",
    "            'reg_alpha': 2,\n",
    "            'reg_lambda': 2\n",
    "        },\n",
    "        'preprocess': lambda x: x.fillna(-999)\n",
    "    },\n",
    "    \"LightGBM\": {\n",
    "        'model': LGBMClassifier,\n",
    "        'params': {\n",
    "            'n_estimators': 5,\n",
    "            'max_depth': 1,\n",
    "            'learning_rate': 0.8,\n",
    "            'subsample': 0.3,\n",
    "            'colsample_bytree': 0.1,\n",
    "            'random_state': RANDOM_SEED,\n",
    "            'min_child_samples': 50,\n",
    "            'reg_alpha': 3,\n",
    "            'reg_lambda': 3\n",
    "        },\n",
    "        'preprocess': lambda x: x.fillna(999999)\n",
    "    },\n",
    "    \"GradientBoosting\": {\n",
    "        'model': GradientBoostingClassifier,\n",
    "        'params': {\n",
    "            'n_estimators': 3,\n",
    "            'max_depth': 2,\n",
    "            'learning_rate': 0.4,\n",
    "            'subsample': 0.5,\n",
    "            'min_samples_split': 9,\n",
    "            'min_samples_leaf': 10,\n",
    "            'max_features': 0.3,\n",
    "            'validation_fraction': 0.1,\n",
    "            'n_iter_no_change': 2\n",
    "        },\n",
    "        'preprocess': lambda x: x.fillna(-999999)\n",
    "    }\n",
    "}\n",
    "\n",
    "# Evaluation metrics\n",
    "scoring = {\n",
    "    'accuracy': 'accuracy',\n",
    "    'balanced_accuracy': 'balanced_accuracy',\n",
    "    'precision': 'precision',\n",
    "    'recall': 'recall',\n",
    "    'f1': 'f1',\n",
    "    'roc_auc': 'roc_auc',\n",
    "    'neg_mean_squared_error': 'neg_mean_squared_error',\n",
    "    'neg_mean_absolute_error': 'neg_mean_absolute_error'\n",
    "}\n",
    "\n",
    "# Create output directory\n",
    "os.makedirs('Model_Results', exist_ok=True)\n",
    "\n",
    "# Initialize KFold\n",
    "kf = KFold(n_splits=10, shuffle=True, random_state=RANDOM_SEED)\n",
    "\n",
    "# Evaluate each model with 10-fold cross validation\n",
    "all_fold_results = []\n",
    "summary_results = []\n",
    "\n",
    "for model_name, config in models.items():\n",
    "    print(f\"\\n=== Evaluating {model_name} ===\")\n",
    "    \n",
    "    # Preprocess data\n",
    "    X_processed = config['preprocess'](X)\n",
    "    X_train_processed = config['preprocess'](X_train)\n",
    "    X_test_processed = config['preprocess'](X_test)\n",
    "    \n",
    "    # Initialize model\n",
    "    model = config['model'](**config['params'])\n",
    "    \n",
    "    # Store fold results for this model\n",
    "    model_fold_results = []\n",
    "    \n",
    "    # Manual cross-validation to get per-fold results\n",
    "    for fold, (train_idx, val_idx) in enumerate(kf.split(X_processed, y)):\n",
    "        X_train_fold, X_val_fold = X_processed.iloc[train_idx], X_processed.iloc[val_idx]\n",
    "        y_train_fold, y_val_fold = y.iloc[train_idx], y.iloc[val_idx]\n",
    "        \n",
    "        model.fit(X_train_fold, y_train_fold)\n",
    "        y_pred_fold = model.predict(X_val_fold)\n",
    "        y_prob_fold = model.predict_proba(X_val_fold)[:, 1]\n",
    "        \n",
    "        # Calculate metrics for this fold\n",
    "        fold_metrics = {\n",
    "            'Model': model_name,\n",
    "            'Fold': fold + 1,\n",
    "            'Accuracy': accuracy_score(y_val_fold, y_pred_fold),\n",
    "            'Balanced_Accuracy': balanced_accuracy_score(y_val_fold, y_pred_fold),\n",
    "            'Precision': precision_score(y_val_fold, y_pred_fold),\n",
    "            'Recall': recall_score(y_val_fold, y_pred_fold),\n",
    "            'F1': f1_score(y_val_fold, y_pred_fold),\n",
    "            'ROC_AUC': roc_auc_score(y_val_fold, y_prob_fold),\n",
    "            'RMSE': np.sqrt(mean_squared_error(y_val_fold, y_prob_fold)),\n",
    "            'MAE': mean_absolute_error(y_val_fold, y_prob_fold)\n",
    "        }\n",
    "        \n",
    "        model_fold_results.append(fold_metrics)\n",
    "    \n",
    "    # Save per-fold results for this model\n",
    "    fold_df = pd.DataFrame(model_fold_results)\n",
    "    fold_df.to_csv(f'{model_name}_fold_results.csv', index=False)\n",
    "    all_fold_results.append(fold_df)\n",
    "    \n",
    "    # Also run cross_validate for summary statistics\n",
    "    cv_results = cross_validate(\n",
    "        estimator=model,\n",
    "        X=X_processed,\n",
    "        y=y,\n",
    "        cv=10,\n",
    "        scoring=scoring,\n",
    "        n_jobs=N_JOBS,\n",
    "        return_train_score=True\n",
    "    )\n",
    "    \n",
    "    # Fit on training data\n",
    "    model.fit(X_train_processed, y_train)\n",
    "    y_pred = model.predict(X_test_processed)\n",
    "    y_prob = model.predict_proba(X_test_processed)[:, 1]\n",
    "    \n",
    "    # Calculate summary metrics\n",
    "    metrics = {\n",
    "        'Model': model_name,\n",
    "        'Parameters': str(config['params']),\n",
    "        'CV_Folds': 10,\n",
    "        'Train_Accuracy_Mean': np.mean(cv_results['train_accuracy']),\n",
    "        'Test_Accuracy_Mean': np.mean(cv_results['test_accuracy']),\n",
    "        'Train_Accuracy_Std': np.std(cv_results['train_accuracy']),\n",
    "        'Test_Accuracy_Std': np.std(cv_results['test_accuracy']),\n",
    "        'Train_Balanced_Accuracy_Mean': np.mean(cv_results['train_balanced_accuracy']),\n",
    "        'Test_Balanced_Accuracy_Mean': np.mean(cv_results['test_balanced_accuracy']),\n",
    "        'Train_Precision_Mean': np.mean(cv_results['train_precision']),\n",
    "        'Test_Precision_Mean': np.mean(cv_results['test_precision']),\n",
    "        'Train_Recall_Mean': np.mean(cv_results['train_recall']),\n",
    "        'Test_Recall_Mean': np.mean(cv_results['test_recall']),\n",
    "        'Train_F1_Mean': np.mean(cv_results['train_f1']),\n",
    "        'Test_F1_Mean': np.mean(cv_results['test_f1']),\n",
    "        'Train_ROC_AUC_Mean': np.mean(cv_results['train_roc_auc']),\n",
    "        'Test_ROC_AUC_Mean': np.mean(cv_results['test_roc_auc']),\n",
    "        'Test_Set_Accuracy': accuracy_score(y_test, y_pred),\n",
    "        'Test_Set_Balanced_Accuracy': balanced_accuracy_score(y_test, y_pred),\n",
    "        'Test_Set_Precision': precision_score(y_test, y_pred),\n",
    "        'Test_Set_Recall': recall_score(y_test, y_pred),\n",
    "        'Test_Set_F1': f1_score(y_test, y_pred),\n",
    "        'Test_Set_AUC_ROC': roc_auc_score(y_test, y_prob),\n",
    "        'Test_Set_RMSE': np.sqrt(mean_squared_error(y_test, y_prob)),\n",
    "        'Test_Set_MAE': mean_absolute_error(y_test, y_prob)\n",
    "    }\n",
    "    \n",
    "    summary_results.append(metrics)\n",
    "    \n",
    "    # Print summary\n",
    "    print(f\"\\nModel: {model_name}\")\n",
    "    print(f\"Parameters: {config['params']}\")\n",
    "    print(\"\\nCross-validation accuracy:\")\n",
    "    print(f\"Train: {metrics['Train_Accuracy_Mean']:.4f} ± {metrics['Train_Accuracy_Std']:.4f}\")\n",
    "    print(f\"Test: {metrics['Test_Accuracy_Mean']:.4f} ± {metrics['Test_Accuracy_Std']:.4f}\")\n",
    "    print(\"\\nTest set classification report:\")\n",
    "    print(classification_report(y_test, y_pred))\n",
    "\n",
    "# Save all fold results\n",
    "all_folds_df = pd.concat(all_fold_results)\n",
    "all_folds_df.to_csv('all_models_fold_results.csv', index=False)\n",
    "\n",
    "# Save summary results\n",
    "summary_df = pd.DataFrame(summary_results)\n",
    "summary_df.to_csv('model_performance_summary.csv', index=False)\n",
    "\n",
    "print(\"\\n=== Evaluation complete ===\")\n",
    "print(\"Results saved in 'Model_Results' directory\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load results\n",
    "df = pd.read_csv('all_models_fold_results.csv')\n",
    "\n",
    "# Drop rows where Model is NaN\n",
    "df = df.dropna(subset=['Model'])\n",
    "\n",
    "# Now proceed\n",
    "metrics = ['Accuracy', 'Balanced_Accuracy', 'Precision', 'Recall', 'F1', 'ROC_AUC']\n",
    "models = df['Model'].unique()\n",
    "\n",
    "# Compute mean per model per metric\n",
    "mean_scores = df.groupby('Model')[metrics].mean()\n",
    "\n",
    "# Prepare Radar Chart\n",
    "labels = metrics\n",
    "num_vars = len(labels)\n",
    "\n",
    "# Compute angle for each axis\n",
    "angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()\n",
    "angles += angles[:1]  # complete loop\n",
    "\n",
    "# Start plotting\n",
    "fig, ax = plt.subplots(figsize=(8,8), subplot_kw=dict(polar=True))\n",
    "\n",
    "for model in models:\n",
    "    values = mean_scores.loc[model].tolist()\n",
    "    values += values[:1]  # complete loop\n",
    "    ax.plot(angles, values, label=model)\n",
    "    ax.fill(angles, values, alpha=0.1)\n",
    "\n",
    "# Add labels\n",
    "ax.set_xticks(angles[:-1])\n",
    "ax.set_xticklabels(labels, size=12)\n",
    "\n",
    "# Set title and legend\n",
    "plt.title('Model Comparison Across Metrics', size=16, y=1.1)\n",
    "ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))\n",
    "\n",
    "# Display\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "data = {\n",
    "    'Model': ['CardioForest'] * 10 + ['XGBoost'] * 10 + ['LightGBM'] * 10 + ['GradientBoosting'] * 10,\n",
    "    'Fold': list(range(1, 11)) * 4,\n",
    "    'RMSE': [\n",
    "        0.251277373, 0.246827624, 0.25435399, 0.242595921, 0.252222666, 0.249007959, 0.241457294, 0.256696882, 0.244734536, 0.246240657,\n",
    "        0.312358145, 0.29413596, 0.306670218, 0.292872235, 0.308053231, 0.300870523, 0.295996722, 0.312399717, 0.295000142, 0.304034757,\n",
    "        0.351450273, 0.339869388, 0.353028976, 0.335494485, 0.35013639, 0.341805012, 0.344916338, 0.349676223, 0.335514799, 0.347297609,\n",
    "        0.275703509, 0.237860914, 0.295163466, 0.242319914, 0.343568679, 0.248404901, 0.224961985, 0.234555587, 0.305376512, 0.228012208\n",
    "    ],\n",
    "    'MAE': [\n",
    "        0.190894043, 0.18926961, 0.192631041, 0.188452525, 0.19136822, 0.190318725, 0.187062261, 0.19281377, 0.188677701, 0.188985499,\n",
    "        0.20685561, 0.194210172, 0.205378026, 0.198772952, 0.202913836, 0.200893492, 0.199951589, 0.207820818, 0.197869122, 0.203201398,\n",
    "        0.246100417, 0.236650367, 0.245937332, 0.234540687, 0.244199586, 0.238731384, 0.242382741, 0.245079374, 0.234810662, 0.244052581,\n",
    "        0.196533959, 0.163107585, 0.211897167, 0.165661638, 0.254912706, 0.17665235, 0.153220139, 0.143426632, 0.223387009, 0.151414387\n",
    "    ]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Set up the plot\n",
    "fig, ax = plt.subplots(figsize=(14, 8))\n",
    "\n",
    "colors = {\n",
    "    'CardioForest': '#1f77b4',\n",
    "    'XGBoost': '#ff7f0e',\n",
    "    'LightGBM': '#2ca02c',\n",
    "    'GradientBoosting': '#d62728'\n",
    "}\n",
    "\n",
    "# Plot each model\n",
    "for model, color in colors.items():\n",
    "    model_data = df[df['Model'] == model]\n",
    "    ax.plot(model_data['Fold'], model_data['RMSE'], label=f'{model} RMSE', marker='o', color=color, linestyle='-')\n",
    "    ax.plot(model_data['Fold'], model_data['MAE'], label=f'{model} MAE', marker='s', color=color, linestyle='--')\n",
    "\n",
    "# Styling\n",
    "ax.set_xlabel('Fold', fontsize=16, fontname='Times New Roman')\n",
    "ax.set_ylabel('Error Value', fontsize=16, fontname='Times New Roman')\n",
    "ax.set_title('Error Metric Evaluation Across Folds', fontsize=18, fontweight='bold', fontname='Times New Roman')\n",
    "ax.tick_params(axis='both', labelsize=12)\n",
    "ax.legend(fontsize=12)\n",
    "ax.set_facecolor('white')\n",
    "fig.patch.set_facecolor('white')\n",
    "plt.xticks(np.arange(1, 11, 1))\n",
    "plt.yticks(fontsize=12, fontname='Times New Roman')\n",
    "\n",
    "# Remove grid\n",
    "ax.grid(False)\n",
    "\n",
    "# High resolution save\n",
    "plt.tight_layout()\n",
    "plt.savefig('error_metrics_across_folds.png', dpi=600)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from math import pi\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n",
    "from matplotlib import rcParams\n",
    "\n",
    "rcParams['font.family'] = 'Times New Roman'\n",
    "rcParams['font.size'] = 14\n",
    "\n",
    "\n",
    "models = {\n",
    "    \"CardioForest\": {\n",
    "        'model': RandomForestClassifier,\n",
    "        'params': {\n",
    "            'n_estimators': 1000,\n",
    "            'max_depth': 20,\n",
    "            'min_samples_split': 5,\n",
    "            'min_samples_leaf': 2,\n",
    "            'max_features': 0.6,\n",
    "            'class_weight': 'balanced',\n",
    "            'random_state': 42,\n",
    "            'n_jobs': -1,\n",
    "            'bootstrap': True,\n",
    "            'oob_score': True,\n",
    "            'ccp_alpha': 0.01\n",
    "        }\n",
    "    },\n",
    "    \"XGBoost\": {\n",
    "        'model': XGBClassifier,\n",
    "        'params': {\n",
    "            'n_estimators': 10,\n",
    "            'max_depth': 2,\n",
    "            'learning_rate': 0.5,\n",
    "            'subsample': 0.4,\n",
    "            'colsample_bytree': 0.2,\n",
    "            'random_state': 42,\n",
    "            'gamma': 3,\n",
    "            'reg_alpha': 2,\n",
    "            'reg_lambda': 2\n",
    "        }\n",
    "    },\n",
    "    \"LightGBM\": {\n",
    "        'model': LGBMClassifier,\n",
    "        'params': {\n",
    "            'n_estimators': 5,\n",
    "            'max_depth': 1,\n",
    "            'learning_rate': 0.8,\n",
    "            'subsample': 0.3,\n",
    "            'colsample_bytree': 0.1,\n",
    "            'random_state': 42,\n",
    "            'min_child_samples': 50,\n",
    "            'reg_alpha': 3,\n",
    "            'reg_lambda': 3\n",
    "        }\n",
    "    },\n",
    "    \"GradientBoosting\": {\n",
    "        'model': GradientBoostingClassifier,\n",
    "        'params': {\n",
    "            'n_estimators': 3,\n",
    "            'max_depth': 2,\n",
    "            'learning_rate': 0.4,\n",
    "            'subsample': 0.5,\n",
    "            'min_samples_split': 9,\n",
    "            'min_samples_leaf': 10,\n",
    "            'max_features': 0.3,\n",
    "            'validation_fraction': 0.1,\n",
    "            'n_iter_no_change': 2\n",
    "        }\n",
    "    }\n",
    "}\n",
    "\n",
    "\n",
    "results = pd.read_csv('all_models_fold_results.csv')\n",
    "\n",
    "mean_metrics = results.groupby('Model').mean().reset_index()\n",
    "\n",
    "# Select metrics for radar plot\n",
    "metrics = ['Accuracy', 'Balanced_Accuracy', 'Precision', 'Recall', 'F1', 'ROC_AUC']\n",
    "\n",
    "# Normalize metrics to 0-1 scale\n",
    "normalized = mean_metrics[metrics].apply(lambda x: (x - x.min()) / (x.max() - x.min()))\n",
    "normalized['Model'] = mean_metrics['Model']\n",
    "\n",
    "fitting_types = {\n",
    "    \"CardioForest\": \"Best Fit\",\n",
    "    \"XGBoost\": \"Underfit\",\n",
    "    \"LightGBM\": \"Underfit\",\n",
    "    \"GradientBoosting\": \"Overfit\"\n",
    "}\n",
    "\n",
    "# Create radar plot\n",
    "plt.figure(figsize=(10, 8))\n",
    "ax = plt.subplot(111, polar=True)\n",
    "\n",
    "# Calculate angles for radar plot\n",
    "categories = metrics\n",
    "N = len(categories)\n",
    "angles = [n / float(N) * 2 * pi for n in range(N)]\n",
    "angles += angles[:1]\n",
    "\n",
    "# Configure plot\n",
    "ax.set_theta_offset(pi/2)\n",
    "ax.set_theta_direction(-1)\n",
    "plt.xticks(angles[:-1], categories, size=14)\n",
    "ax.set_rlabel_position(0)\n",
    "plt.yticks([0.2, 0.4, 0.6, 0.8, 1.0], [\"0.2\", \"0.4\", \"0.6\", \"0.8\", \"1.0\"], size=12)\n",
    "plt.ylim(0, 1.1)\n",
    "\n",
    "# Plot each model\n",
    "colors = {'Best Fit': 'green', 'Overfit': 'red', 'Underfit': 'blue'}\n",
    "for model in normalized['Model']:\n",
    "    values = normalized[normalized['Model'] == model][metrics].values.flatten().tolist()\n",
    "    values += values[:1]\n",
    "    fit_type = fitting_types[model]\n",
    "    \n",
    "    ax.plot(angles, values, linewidth=2, linestyle='solid', \n",
    "            label=f\"{model} ({fit_type})\", color=colors[fit_type])\n",
    "    ax.fill(angles, values, alpha=0.1, color=colors[fit_type])\n",
    "\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('model_fitting_analysis_high_quality.png', dpi=600, bbox_inches='tight')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2400x1800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# Load data\n",
    "df = pd.read_csv('/mimic-iv-ecg-diagnostic-electrocardiogram-matched-subset-1.0/merged_ecg_data_cleaned.csv')\n",
    "\n",
    "# Define features and label\n",
    "features = ['rr_interval', 'p_onset', 'p_end', 'qrs_onset', 'qrs_end', 't_end',\n",
    "            'p_axis', 'qrs_axis', 't_axis', 'qrs_duration']\n",
    "X = df[features]\n",
    "y = df['wct_label_encoded']\n",
    "\n",
    "# Train CardioForest model\n",
    "cardioforest_params = {\n",
    "    'n_estimators': 1000,\n",
    "    'max_depth': 20,\n",
    "    'min_samples_split': 5,\n",
    "    'min_samples_leaf': 2,\n",
    "    'max_features': 0.6,\n",
    "    'class_weight': 'balanced',\n",
    "    'random_state': 42,\n",
    "    'n_jobs': -1,\n",
    "    'bootstrap': True,\n",
    "    'oob_score': True,\n",
    "    'ccp_alpha': 0.01\n",
    "}\n",
    "cardioforest = RandomForestClassifier(**cardioforest_params)\n",
    "cardioforest.fit(X, y)\n",
    "\n",
    "# Predict WCT\n",
    "predictions = cardioforest.predict(X)\n",
    "\n",
    "# Count predictions\n",
    "normal_count = (predictions == 0).sum()\n",
    "wct_count = (predictions == 1).sum()\n",
    "\n",
    "# Save detection rate table\n",
    "detection_rate = {\n",
    "    'Category': ['Normal', 'WCT'],\n",
    "    'Count': [normal_count, wct_count],\n",
    "    'Detection_Rate (%)': [\n",
    "        normal_count / len(predictions) * 100,\n",
    "        wct_count / len(predictions) * 100\n",
    "    ]\n",
    "}\n",
    "detection_df = pd.DataFrame(detection_rate)\n",
    "detection_df.to_csv('wct_detection_rates.csv', index=False)\n",
    "\n",
    "# 9. Generate and save high-quality figure (Pie Chart version)\n",
    "plt.figure(figsize=(8,6), dpi=300)\n",
    "\n",
    "# Pie chart\n",
    "labels = ['Normal', 'WCT']\n",
    "sizes = [normal_count, wct_count]\n",
    "colors = ['skyblue', 'salmon']\n",
    "explode = (0.05, 0.1)  # Slightly explode both slices\n",
    "\n",
    "plt.pie(\n",
    "    sizes, \n",
    "    labels=labels, \n",
    "    colors=colors,\n",
    "    explode=explode,\n",
    "    autopct='%1.1f%%',\n",
    "    shadow=True, \n",
    "    startangle=140,\n",
    "    textprops={'fontsize': 12, 'fontname': 'Times New Roman'}\n",
    ")\n",
    "\n",
    "plt.title('WCT vs Normal Predictions', fontsize=18, fontname='Times New Roman')\n",
    "\n",
    "# Save the figure\n",
    "plt.tight_layout()\n",
    "plt.savefig('wct_prediction_pie_chart.png')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CardioForest model saved as 'cardioforest_model.pkl'\n"
     ]
    }
   ],
   "source": [
    "import joblib\n",
    "\n",
    "# Save the model\n",
    "joblib.dump(cardioforest, 'cardioforest_model.pkl')\n",
    "print(\"CardioForest model saved as 'cardioforest_model.pkl'\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load the dataset\n",
    "df = pd.read_csv(\"/mimic-iv-ecg-diagnostic-electrocardiogram-matched-subset-1.0/merged_ecg_data_cleaned.csv\")\n",
    "\n",
    "# Features to include in the boxplot\n",
    "features = ['rr_interval', 'p_onset', 'p_end', 'qrs_onset', 'qrs_end', \n",
    "            't_end', 'p_axis', 'qrs_axis', 't_axis', 'qrs_duration']\n",
    "\n",
    "# Set the figure size and style\n",
    "plt.figure(figsize=(16, 10))\n",
    "sns.set_style(\"white\")  # Remove grid\n",
    "sns.set_context(\"notebook\", font_scale=1.6)  # Increase all font sizes\n",
    "\n",
    "# Melt the dataframe for seaborn\n",
    "df_melted = df[features].melt(var_name=\"ECG Feature\", value_name=\"Value\")\n",
    "\n",
    "# Create the boxplot\n",
    "ax = sns.boxplot(data=df_melted, x=\"Value\", y=\"ECG Feature\", orient=\"h\", fliersize=3, linewidth=1.3)\n",
    "\n",
    "# Set x-axis to start at 0\n",
    "plt.xlim(left=0)\n",
    "\n",
    "# Customize labels and title\n",
    "plt.xlabel(\"Value\", fontsize=18, weight='bold')\n",
    "plt.ylabel(\"ECG Feature\", fontsize=18, weight='bold')\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "\n",
    "# Tight layout and save\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"ecg_feature_boxplot_high_quality.png\", dpi=300)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
